ActionPlan: Future-Aware Streaming Motion Synthesis via Frame-Level Action Planning

We present ActionPlan, a unified motion diffusion frame-work that bridges real-time streaming with high-quality offline genera-tion within a single model. The core idea is to introduce a per-frameaction plan: the model predicts frame-level text latents that act as densesemantic anchors throughout denoising, and uses them to denoise thefull motion sequence with combined semantic and motion cues. To sup-port this structured workflow, we design latent-specific diffusion steps,allowing each motion latent to be denoised independently and sampledin flexible orders at inference. As a result, ActionPlan can run in ahistory-conditioned, future-aware mode for real-time streaming, whilealso supporting high-quality offline generation. The same mechanism fur-ther enables zero-shot motion editing and in-betweening without addi-tional models. Experiments demonstrate that our real-time streaming is5.25× faster while also achieving 18% motion quality improvement overthe best previous method in terms of FID.∗ †Equal contribution. Corresponding author